Liquid properties of dimethyl ether from molecular dynamics simulations using<i>Ab Initio</i>force fields

Wang, Shi-Bao; Li, Arvin Huang-Te; Chao, Sheng D.

doi:10.1002/jcc.22930

Cited by 10 publications

(4 citation statements)

References 36 publications

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“…In the past decade, we have witnessed an advancement in using quantum chemistry-calculated energy data to build potential energy surfaces (PESs) in the task of force field (FF) constructions [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. In particular, it is now a routine calculation task to employ highly correlated ab initio methods, such as the second-order Møller–Plesset perturbation theory (MP2), to obtain accurate energy data for small molecular dimers with the number of atoms being less than about 50.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Force Field for Bio-Macromolecular Modeling Based on Quantum Chemistry-Calculated Interaction Energy Datasets

Fan,

Chao

2024

Bioengineering

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Accurate energy data from noncovalent interactions are essential for constructing force fields for molecular dynamics simulations of bio-macromolecular systems. There are two important practical issues in the construction of a reliable force field with the hope of balancing the desired chemical accuracy and working efficiency. One is to determine a suitable quantum chemistry level of theory for calculating interaction energies. The other is to use a suitable continuous energy function to model the quantum chemical energy data. For the first issue, we have recently calculated the intermolecular interaction energies using the SAPT0 level of theory, and we have systematically organized these energies into the ab initio SOFG-31 (homodimer) and SOFG-31-heterodimer datasets. In this work, we re-calculate these interaction energies by using the more advanced SAPT2 level of theory with a wider series of basis sets. Our purpose is to determine the SAPT level of theory proper for interaction energies with respect to the CCSD(T)/CBS benchmark chemical accuracy. Next, to utilize these energy datasets, we employ one of the well-developed machine learning techniques, called the CLIFF scheme, to construct a general-purpose force field for biomolecular dynamics simulations. Here we use the SOFG-31 dataset and the SOFG-31-heterodimer dataset as the training and test sets, respectively. Our results demonstrate that using the CLIFF scheme can reproduce a diverse range of dimeric interaction energy patterns with only a small training set. The overall errors for each SAPT energy component, as well as the SAPT total energy, are all well below the desired chemical accuracy of ~1 kcal/mol.

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Section: Introductionmentioning

confidence: 99%

A Machine Learning Force Field for Bio-Macromolecular Modeling Based on Quantum Chemistry-Calculated Interaction Energy Datasets

Fan,

Chao

2024

Bioengineering

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“…The past two decades have witnessed a remarkable advancement of using ab initio data to build ML potentials in conventional force field (FF) constructions [35][36][37][38][39][40][41][42][43][44][45]. In particular, for small molecular dimers (roughly less than 50 atoms involved), highly correlated first-principles quantum chemistry methods, such as coupled-cluster (CC) theory, have been routinely calculated, with data collected that can be used to provide benchmark accuracy, including ab initio data to calibrate other less accurate but more efficient calculation methods, such as the density functional theory (DFT).…”

Section: Introductionmentioning

confidence: 99%

Intermolecular Non-Bonded Interactions from Machine Learning Datasets

Chen,

Chao

2023

Molecules

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Accurate determination of intermolecular non-covalent-bonded or non-bonded interactions is the key to potentially useful molecular dynamics simulations of polymer systems. However, it is challenging to balance both the accuracy and computational cost in force field modelling. One of the main difficulties is properly representing the calculated energy data as a continuous force function. In this paper, we employ well-developed machine learning techniques to construct a general purpose intermolecular non-bonded interaction force field for organic polymers. The original ab initio dataset SOFG-31 was calculated by us and has been well documented, and here we use it as our training set. The CLIFF kernel type machine learning scheme is used for predicting the interaction energies of heterodimers selected from the SOFG-31 dataset. Our test results show that the overall errors are well below the chemical accuracy of about 1 kcal/mol, thus demonstrating the promising feasibility of machine learning techniques in force field modelling.

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“…Because many popular EFFs utilize extensive experimental data in their model constructions, the efficacy in reproducing experiments deteriorates very quickly once the models are used outside the original training sets. More fundamental chemical models (usually called ab initio force fields (AIFFs), to distinguish them from EFFs) are mainly based on quantum chemistry calculations with, hopefully, minimum inputs from experiments. − Most current generation force fields have employed various levels of potential energy data from electronic structure calculations, which are usually collected in the form of numerical data sets. These interaction energy data sets not only are useful for designing universal force fields but also serve as a benchmark for testing and/or training lower-level but more computationally efficient calculation methods, such as the electronic density functional theory (DFT). − Therefore, it is a continuing effort to develop comprehensive data sets of accurate intermolecular interaction energies based on high-level quantum chemistry calculations. − …”

Section: Introductionmentioning

confidence: 99%

A Minimum Quantum Chemistry CCSD(T)/CBS Data Set of Dimeric Interaction Energies for Small Organic Functional Groups: Heterodimers

2022

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We extend our previous quantum chemistry calculations of interaction energies for 31 homodimers of small organic functional groups (the SOFG-31 data set) by including 239 heterodimers with monomers selected within the SOFG-31 data set, thus resulting in the SOFG-31+239 data set. The minimum-level theoretical scheme contains (1) the basis set superposition error corrected supermolecule (BSSE-SM) approach for intermolecular interactions; (2) the second-order Møller–Plesset perturbation theory (MP2) with the Dunning’s aug-cc-pVXZ (X = D, T, Q) basis sets for the geometry optimization and correlation energy calculations; and (3) the single-point energy calculations with the coupled cluster with single, double, and perturbative triple excitations method at the complete basis set limit [CCSD(T)/CBS] using the well-tested extrapolation methods for the MP2 energy calibrations. In addition, we have performed a parallel series of energy decomposition calculations based on the symmetry adapted perturbation theory (SAPT) in order to gain chemical insights. That the above procedure cannot be further reduced has been proven to be very crucial for constructing reliable data sets of interaction energies. The calculated CCSD(T)/CBS interaction energy data can serve as a benchmark for testing or training less accurate but more efficient calculation methods, such as the electronic density functional theory. As an application, we employ a segmental SAPT model previously developed for the SOFG-31 data set to predict binding energies of large heterodimer complexes. These model energy “quanta” can be used in coarse-grained molecular dynamics simulations by avoiding large-scale calculations.

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Liquid properties of dimethyl ether from molecular dynamics simulations usingAb Initioforce fields

Cited by 10 publications

References 36 publications

A Machine Learning Force Field for Bio-Macromolecular Modeling Based on Quantum Chemistry-Calculated Interaction Energy Datasets

A Machine Learning Force Field for Bio-Macromolecular Modeling Based on Quantum Chemistry-Calculated Interaction Energy Datasets

Intermolecular Non-Bonded Interactions from Machine Learning Datasets

A Minimum Quantum Chemistry CCSD(T)/CBS Data Set of Dimeric Interaction Energies for Small Organic Functional Groups: Heterodimers

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